Nazim Kemal Ure

Comparison of Fixed and Variable Pitch Actuators for Agile Quadrotors

This paper presents the design, analysis and experimental testing of a variablepitch quadrotor. A custom in-lab built quadrotor with on-board attitude stabilization is developed and tested. An analysis of the dynamic differences in thrust output between a fixed-pitch and variable-pitch propeller is given and validated with simulation and experimental results. It is shown that variable-pitch actuation has significant advantages over the conventional fixed-pitch configuration, including increased thrust rate of change, decreased control saturation, and the ability to quickly and efficiently reverse thrust. These advantages result in improved quadrotor tracking of linear and angular acceleration command inputs in both simulation and hardware testing. The benefits should enable more aggressive and aerobatic flying with the variable-pitch quadrotor than with standard fixed-pitch actuation, while retaining much of the mechanical simplicity and robustness of the fixed-pitch quadrotor.

Experimental Results of Concurrent Learning Adaptive Controllers

Commonly used Proportional-Integral-Derivative based UAV ight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di erent. Concurrent learning adaptive control uses speci cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT’s RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.

Design of a multi modal control framework for agile maneuvering UCAV

This paper discusses the structure of a multi modal control framework for generation and control of aggressive maneuver profiles for agile unmanned vehicles. It is shown that any arbitrary flight maneuver can be decomposed into simpler flight modes and modal parameters, which are derived from combat maneuvers and aerobatics. Feasible maneuver generation problem is complicated by both sequence of the maneuver modes and envelope constraints on control inputs. These problems are solved by developing mode transition rules and a set of agility metrics that bounds the domain. Overall system with flight modes, transition conditions and domains is shown to be a finite state machine which spans full flight envelope of maneuvers of UCAV, where local control of each mode results in control of full flight maneuver. Thus, maneuver controlling problem is reduced into lower dimensional maneuver mode and parameter search

MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems

Cyber-Physical Systems (CPSs) refer to engineering platforms that rely on the integration of physical systems with control, computation, and communication technologies. Autonomous vehicles are instances of CPSs that are rapidly growing with applications in many domains. Due to the integration of physical systems with computational sensing, planning, and learning in CPSs, hardware-in-the-loop experiments are an essential step for transitioning from simulations to real-world experiments. This paper proposes an architecture for rapid prototyping of CPSs that has been developed in the Aerospace Controls Laboratory at the Massachusetts Institute of Technology. This system, referred to as MAR-CPS (Measurable Augmented Reality for Prototyping Cyber-Physical Systems), includes physical vehicles and sensors, a motion capture technology, a projection system, and a communication network. The role of the projection system is to augment a physical laboratory space with 1) autonomous vehicles’ beliefs and 2) a simulated mission environment, which in turn will be measured by physical sensors on the vehicles. The main focus of this method is on rapid design of planning, perception, and learning algorithms for autonomous single-agent or multi-agent systems. Moreover, the proposed architecture allows researchers to project a simulated counterpart of outdoor environments in a controlled, indoor space, which can be crucial when testing in outdoor environments is disfavored due to safety, regulatory, or monetary concerns. We discuss the issues related to the design and implementation of MAR-CPS and demonstrate its real-time behavior in a variety of problems in autonomy, such as motion planning, multi-robot coordination, and learning spatio-temporal fields.

Autonomous Control of Unmanned Combat Air Vehicles: Design of a Multimodal Control and Flight Planning Framework for Agile Maneuvering

There is a growing demand for unmanned air vehicles (UAVs) with combat capabilities in battlefield scenarios [1]. Whether this capability is for evasive maneuvers or for flying attack patterns, unmanned combat air vehicles (UCAVs) are expected to operate in dense and often threatening environments that require aggressive trajectory planning and controls [1]. These trajectories often require the use of maneuvering capability over the full flight envelope of the aircraft. Examples of such trajectories are high-g turns and high angle-of-attack maneuvers. This article presents the development of a multimodal flight control and flight path planning scheme that allows the vehicle to autonomously perform agile maneuvers over its full flight envelope. The key element of this scheme is the maneuver decomposition methodology, which aims to reduce the complexity of the planning and control problems for UCAVs. This article demonstrates how a parameterized family of maneuver modes for a UCAV can be developed systematically and how an arbitrary agile maneuver can be decomposed into simpler segments. In addition, the article presents a multimodal flight control scheme in which each of the maneuver modes is controlled locally by a sliding mode controller. The overall capability of the system is demonstrated in challenging scenarios such as navigation in dense environments and autonomous execution of aerobatics competition sequences.

Infrastructure Development for Ground-Based Separation Assurance with Optional Automation

The increasing volume of traffic in the air transportation is leading to excessive workload on air traffic controllers. Developing automated air traffic management (ATM) tools is a critical technology for reducing the workload of air traffic controllers and hence increasing the airspace capacity. The existing approaches to automated ATM either use overly-simplified air traffic and aircraft dynamics models to reduce computational complexity or end up being computationally intractable for large scale ATM scenarios. Our previous work presented a new hybrid system description of modeling the decision process of the air traffic controllers in en-route and approach operations. The emulation of air traffic controller decision process in the hybrid model provides realistic conflict resolution maneuvers in 3D, while being computationally tractable. This work builds upon the developed automated separation assurance algorithm, and investigates the application of the algorithm on real flight data and the integration with avionics systems. The algorithm is validated on the real flight plan data for the Istanbul region extracted from the ALLFT+ dataset provided by EUROCONTROL, which includes over 7000 flights in a 96-hour period. The developed algorithm is also integrated into a Boeing 737--800 flight deck simulator with a custom radar display and additional human in the loop simulations are ran to demonstrate the applicability of the developed approach to existing avionics systems.

Distributed Learning for Large-scale Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multiagent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. The algorithms developed here are validated on a large-scale persistent search and track flight test with mixed real/virtual agents.

Hybrid systems modeling and automated air traffic control for three-dimensional separation assurance

The increasing volume of traffic in the air transportation is leading to excessive workload on air traffic controllers. Developing automated air traffic management (ATM) tools is a critical technology in reducing the workload of air traffic controllers and hence increasing the airspace capacity. The existing approaches to automated ATM either use overly-simplified air traffic and aircraft dynamics models to reduce computational complexity or end up being computationally intractable for large-scale ATM scenarios. This paper presents a new hybrid system description of modeling the decision process of the air traffic controllers in en-route and approach operations. The model is based on the domain expertise provided by the State Airport Authority and Air Navigation Service Provider (ANSP) of Turkey. The emulation of air traffic controller decision process in the hybrid model provides realistic conflict resolution maneuvers and separation assurance in 3D, while being computationally tractable. The algorithm has polynomial iteration complexity in the number of waypoints of the aircraft, which makes it scalable to large-scale ATM scenarios with more than 100 aircraft. The algorithm is validated on the real air traffic data over the Istanbul region extracted from the ALLFT+ dataset provided by EUROCONTROL, which includes over 7000 flights in a 96-hour period. The developed algorithm is also integrated into a Boeing 737-800 flight deck simulator with a custom radar display to demonstrate the applicability to existing avionics systems.